Machine learning approaches to convective parametrization

Documentation

The documentation is hosted on github pages: https://nbren12.github.io/uwnet/

Setup

This project uses anaconda to manage the various libraries and packages used. If you do not already have it installed you will need to install miniconda. Once that is installed the environment for this package can be installed by running

make create_environment

Then, you have to activate this environment by running

conda activate uwnet

Downloading the data

The data can be downloaded by running

make data

Name		Name	Last commit message	Last commit date
Latest commit History 915 Commits
.dvc		.dvc
assets		assets
bin		bin
ci		ci
data/processed		data/processed
docs		docs
examples		examples
ext		ext
git-hooks		git-hooks
notebooks		notebooks
setup		setup
src		src
stewart_intro		stewart_intro
tests		tests
uwnet		uwnet
.dockerignore		.dockerignore
.gitignore		.gitignore
.travis.yml		.travis.yml
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
Snakefile		Snakefile
data.nf		data.nf
docker-compose.yml		docker-compose.yml
environment.yml		environment.yml
github_deploy_key_nbren12_uwnet.enc		github_deploy_key_nbren12_uwnet.enc
pytest.ini		pytest.ini
run_climt.py		run_climt.py
setup.py		setup.py
train.nf		train.nf

License

sarenehan/uwnet

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Machine learning approaches to convective parametrization

Documentation

Setup

Downloading the data

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